{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa79c4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "data = [90, 80, 50, 42, 89, 78, 34, 70, 67, 73, 74, 80, 60, 90, 90]\n",
    "sns.distplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.title('Density Plot')\n",
    "plt.xlabel('Score')\n",
    "plt.ylabel('Density')\n",
    "sns.distplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
